Autonomous battery research: Principles of heuristic operando experimentation
This work exposes the systemic inability of conventional operando experiments to capture transient phenomena like dendrite initiation in batteries. To address this, Heuristic Operando experiments are proposed: a framework where an AI pilot leverages physics-based digital twins to actively steer the beamline to predict and deterministically capture rare events. This proactive approach redefines experimental efficiency via an entropy-based metric that prioritises scientific insight per photon, neutron, or muon, serving as a blueprint for trusted autonomous battery laboratories.
Autonomous experimentation
Battery degradation
Operando characterisation
Machine learning (ML)
Digital twins
A framework for heuristic operando experiments where an AI pilot leverages physics-based digital twins to actively steer beamlines and capture stochastic battery failure events.
Emily Lu
Gabriel Perez
Peter Baker
Daniel Irving
Santosh Kumar
Veronica Celorrio
Sylvia Britto
Thomas F. Headen
Miguel Gomez-Gonzalez
Connor Wright
Calum Green
Robert Scott Young
Oleg Kirichek
Ali Mortazavi
Sarah Day
Isabel Antony
Zoe Wright
Thomas Wood
Tim Snow
Jeyan Thiyagalingam
Paul Quinn
Martin Owen Jones
William David
James Le Houx 